Accurate state estimation is an integral part of maintaining safe operating conditions of power distribution systems and serves as input for control functionalities, such as economic dispatch and optimal power flow problems. State estimation can be divided into two categories, one is static state estimation and the other is dynamic state estimation.
In static state estimation, the system state is inferred using only measurements from the current snapshot in time. The motivation behind dynamic state estimation is to use information from prior measurements in addition to the current measurements to make an improved estimate. The dynamic state estimation is a large-scale nonlinear state estimation problem for a practical power distribution system. The solution accuracy and efficiency of dynamic state estimation method plays a crucial role for the success of its real-time applications.
For example, the method described in CN 101615794 A discloses a dynamic state estimation method for an electrical power system based on an unscented Kalman filter (UKF). However, that method requires a state transition model in addition to measurement model to be available. Due to infeasibility for acquiring explicit state transition models for a practical power system, the exponential smoothing based approach was used to fit the state transit model bus by bus. However, the fitting of state transition model suffers from lack of physical meaning and neglects the dependence between states of different buses. Therefore, the UKF that used this state transition model may not provide sufficient accurate estimation results for a complex power system, such as a practical power distribution system.
Accordingly, there is still a need for a system and a method for dynamic state estimation of a power distribution system.